Experimental settings.
In our experiments, we initialize the weights of quantized model with the corresponding pretrained full-precision model. The quantized model is trained for 300 epochs with batch-size 512 and the base learning rate \(2e\!-\!4\) . We do not use warm-up scheme. For all the experiments, we apply LAMB [1]} optimizer with weight decay set as 0. Other training settings follow DeiT [2]} or Swin Transformer [3]}. Note that we use 8-bit for the patch embedding (first) layer and the classification (last) layer following [4]}.
